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The use of propensity scores and observational data to estimate randomized controlled trial generalizability bias
Abstract
Although randomized controlled trials are considered the ‘gold standard’ for clinical studies, the use of exclusion criteria may impact the external validity of the results. It is unknown whether estimators of effect size are biased by excluding a portion of the target population from enrollment. We propose to use observational data to estimate the bias due to enrollment restrictions, which we term generalizability bias. In this paper, we introduce a class of estimators for the generalizability bias and use simulation to study its properties in the presence of non‐constant treatment effects. We find the surprising result that our estimators can be unbiased for the true generalizability bias even when all potentially confounding variables are not measured. In addition, our proposed doubly robust estimator performs well even for mis‐specified models. Copyright © 2013 John Wiley & Sons, Ltd.
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“Failure to do this has resulted in criticisms of prior research on barriers to treatment lacking external validity (Pressler & Kaizar, 2013).”
From Barriers of mental health treatment utilization among first‐year college students: First cross‐national results from the WHO World Mental Health International College Student Initiative (2019) · cites this paper as Pressler & Kaizar, 2013
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Metadata
- Authors
- Pressler, T. R., & Kaizar, E. E.
- Affiliations
- Quantum Health Columbus OH 43235 U.S.A.
- Department of Statistics The Ohio State University Columbus OH 43210 U.S.A.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2013-04
- Volume
- 32
- Issue
- 20
- Pages
- 3552-3568
- ISSN
- 0277-6715
- 1097-0258
- PMC
- PMC3967586
- Cited by
- 32
- References
- 29
- Language
- en
- Licence
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 883 d)
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